{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1ba32dd",
   "metadata": {},
   "source": [
    "# Focal loss vs. cross-entropy loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "816ef63e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install -q tabulate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74690204",
   "metadata": {},
   "source": [
    "In this notebook we study calibration of a model trained with a focal and a cross-entropy loss. While the cross-entropy loss is the gold standard for training classification tasks that want to maximize accuracy, it has been shown [Mukhoti et al., 2020](https://proceedings.neurips.cc/paper/2020/file/aeb7b30ef1d024a76f21a1d40e30c302-Paper.pdf) that the focal loss tends to achieve better calibration errors. In this notebook we compare the two on the [MNIST corrupted](https://www.tensorflow.org/datasets/catalog/mnist_corrupted) data set, for a simple LeNet-5 deep learning model. The hyper-parameter of the focal loss is kept to the default `gamma=2`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e8bd058",
   "metadata": {},
   "source": [
    "## Download, split and process the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb57bf32",
   "metadata": {},
   "source": [
    "First, we download the data from TensorFlow, and split them into training, validation and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7253d1ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/detomma/Library/Caches/pypoetry/virtualenvs/aws-fortuna-ITtrI0mA-py3.9/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
      "Instructions for updating:\n",
      "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/detomma/Library/Caches/pypoetry/virtualenvs/aws-fortuna-ITtrI0mA-py3.9/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
      "Instructions for updating:\n",
      "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds\n",
    "\n",
    "\n",
    "def download(split_range, shuffle=False):\n",
    "    ds = tfds.load(\n",
    "        name=\"mnist_corrupted\",\n",
    "        split=f\"train[{split_range}]\",\n",
    "        as_supervised=True,\n",
    "        shuffle_files=True,\n",
    "    ).map(lambda x, y: (tf.cast(x, tf.float32) / 255.0, y))\n",
    "    if shuffle:\n",
    "        ds = ds.shuffle(10, reshuffle_each_iteration=True)\n",
    "    return ds.batch(128).prefetch(1)\n",
    "\n",
    "\n",
    "train_data_loader, val_data_loader, test_data_loader = (\n",
    "    download(\":80%\", shuffle=True),\n",
    "    download(\"80%:90%\"),\n",
    "    download(\"90%:\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a6193ce",
   "metadata": {},
   "source": [
    "We then convert the data loaders into something that Fortuna can work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "010bddb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fortuna.data import DataLoader\n",
    "\n",
    "train_data_loader = DataLoader.from_tensorflow_data_loader(train_data_loader)\n",
    "val_data_loader = DataLoader.from_tensorflow_data_loader(val_data_loader)\n",
    "test_data_loader = DataLoader.from_tensorflow_data_loader(test_data_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d105c86",
   "metadata": {},
   "source": [
    "## Define and train the calibration model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5004dce1",
   "metadata": {},
   "source": [
    "We define a calibration model and train it with both cross entropy and focal loss. We repeat the task several times to capture the variability of the calibration error. During training, we monitor Brier score and accuracy. Early stopping is enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b3e71696",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch: 6 | loss: 0.01396 | brier: 0.00609 | acc: 1.0:   5%|████▉                                                                                             | 5/100 [00:30<09:43,  6.14s/it]\n",
      "Epoch: 5 | loss: 0.03818 | brier: 0.01845 | acc: 0.98438:   4%|███▊                                                                                          | 4/100 [00:25<10:08,  6.34s/it]\n",
      "Epoch: 5 | loss: 0.01545 | brier: 0.00617 | acc: 1.0:   4%|███▉                                                                                              | 4/100 [00:24<09:50,  6.15s/it]\n",
      "Epoch: 6 | loss: 0.01839 | brier: 0.01182 | acc: 0.99219:   5%|████▋                                                                                         | 5/100 [00:29<09:23,  5.93s/it]\n",
      "Epoch: 7 | loss: 0.01212 | brier: 0.00542 | acc: 0.99219:   6%|█████▋                                                                                        | 6/100 [00:35<09:08,  5.84s/it]\n",
      "Epoch: 6 | loss: 0.02762 | brier: 0.01479 | acc: 0.99219:   5%|████▋                                                                                         | 5/100 [00:30<09:42,  6.13s/it]\n",
      "Epoch: 5 | loss: 0.0231 | brier: 0.01301 | acc: 0.99219:   4%|███▊                                                                                           | 4/100 [00:25<10:01,  6.26s/it]\n",
      "Epoch: 7 | loss: 0.03092 | brier: 0.01977 | acc: 0.98438:   6%|█████▋                                                                                        | 6/100 [00:35<09:22,  5.98s/it]\n",
      "Epoch: 7 | loss: 0.00072 | brier: 3e-05 | acc: 1.0:   6%|██████                                                                                              | 6/100 [00:37<09:43,  6.21s/it]\n",
      "Epoch: 5 | loss: 0.02782 | brier: 0.01507 | acc: 0.99219:   4%|███▊                                                                                          | 4/100 [00:26<10:44,  6.72s/it]\n",
      "Epoch: 8 | loss: 0.02567 | brier: 0.01707 | acc: 0.98438:   7%|██████▌                                                                                       | 7/100 [00:45<10:09,  6.56s/it]\n",
      "Epoch: 6 | loss: 0.01098 | brier: 0.00526 | acc: 1.0:   5%|████▉                                                                                             | 5/100 [00:34<10:53,  6.88s/it]\n",
      "Epoch: 6 | loss: 0.00793 | brier: 0.00165 | acc: 1.0:   5%|████▉                                                                                             | 5/100 [00:34<10:57,  6.92s/it]\n",
      "Epoch: 5 | loss: 0.02423 | brier: 0.01622 | acc: 0.98438:   4%|███▊                                                                                          | 4/100 [00:28<11:24,  7.14s/it]\n",
      "Epoch: 8 | loss: 0.02019 | brier: 0.01146 | acc: 0.99219:   7%|██████▌                                                                                       | 7/100 [00:45<10:03,  6.49s/it]\n",
      "Epoch: 6 | loss: 0.0074 | brier: 0.00207 | acc: 1.0:   5%|████▉                                                                                              | 5/100 [00:34<10:54,  6.89s/it]\n",
      "Epoch: 5 | loss: 0.00571 | brier: 0.00133 | acc: 1.0:   4%|███▉                                                                                              | 4/100 [00:28<11:35,  7.25s/it]\n",
      "Epoch: 5 | loss: 0.01443 | brier: 0.00697 | acc: 0.99219:   4%|███▊                                                                                          | 4/100 [00:29<11:51,  7.42s/it]\n",
      "Epoch: 5 | loss: 0.0709 | brier: 0.03999 | acc: 0.96875:   4%|███▊                                                                                           | 4/100 [00:30<12:05,  7.56s/it]\n",
      "Epoch: 5 | loss: 0.03618 | brier: 0.01817 | acc: 0.99219:   4%|███▊                                                                                          | 4/100 [00:29<11:57,  7.48s/it]\n",
      "Epoch: 5 | loss: 0.02472 | brier: 0.01166 | acc: 0.99219:   4%|███▊                                                                                          | 4/100 [00:30<12:17,  7.68s/it]\n",
      "Epoch: 4 | loss: 0.02267 | brier: 0.01339 | acc: 0.99219:   3%|██▊                                                                                           | 3/100 [00:25<13:41,  8.47s/it]\n",
      "Epoch: 6 | loss: 0.00229 | brier: 0.00031 | acc: 1.0:   5%|████▉                                                                                             | 5/100 [00:37<11:46,  7.44s/it]\n",
      "Epoch: 5 | loss: 0.02517 | brier: 0.01224 | acc: 0.99219:   4%|███▊                                                                                          | 4/100 [00:30<12:19,  7.70s/it]\n",
      "Epoch: 4 | loss: 0.12262 | brier: 0.03931 | acc: 0.97656:   3%|██▊                                                                                           | 3/100 [00:24<13:12,  8.17s/it]\n",
      "Epoch: 7 | loss: 0.02989 | brier: 0.01577 | acc: 0.99219:   6%|█████▋                                                                                        | 6/100 [00:43<11:26,  7.30s/it]\n",
      "Epoch: 5 | loss: 0.01571 | brier: 0.00846 | acc: 0.99219:   4%|███▊                                                                                          | 4/100 [00:30<12:23,  7.75s/it]\n",
      "Epoch: 6 | loss: 0.06796 | brier: 0.03543 | acc: 0.97656:   5%|████▋                                                                                         | 5/100 [00:37<11:44,  7.42s/it]\n",
      "Epoch: 5 | loss: 0.01128 | brier: 0.00467 | acc: 1.0:   4%|███▉                                                                                              | 4/100 [00:31<12:26,  7.78s/it]\n",
      "Epoch: 6 | loss: 0.01947 | brier: 0.00807 | acc: 1.0:   5%|████▉                                                                                             | 5/100 [00:37<11:50,  7.48s/it]\n",
      "Epoch: 6 | loss: 0.01407 | brier: 0.02568 | acc: 0.99219:   5%|████▋                                                                                         | 5/100 [00:37<11:48,  7.46s/it]\n",
      "Epoch: 5 | loss: 0.0609 | brier: 0.03732 | acc: 0.98438:   4%|███▊                                                                                           | 4/100 [00:31<12:35,  7.87s/it]\n",
      "Epoch: 5 | loss: 0.00392 | brier: 0.01428 | acc: 1.0:   4%|███▉                                                                                              | 4/100 [00:31<12:46,  7.98s/it]\n",
      "Epoch: 5 | loss: 0.00826 | brier: 0.02166 | acc: 0.99219:   4%|███▊                                                                                          | 4/100 [00:31<12:42,  7.95s/it]\n",
      "Epoch: 9 | loss: 0.00119 | brier: 0.00547 | acc: 1.0:   8%|███████▊                                                                                          | 8/100 [00:56<10:50,  7.08s/it]\n",
      "Epoch: 4 | loss: 0.01596 | brier: 0.02791 | acc: 0.99219:   3%|██▊                                                                                           | 3/100 [00:25<13:50,  8.56s/it]\n",
      "Epoch: 8 | loss: 0.00851 | brier: 0.02131 | acc: 0.99219:   7%|██████▌                                                                                       | 7/100 [00:48<10:44,  6.93s/it]\n",
      "Epoch: 6 | loss: 0.0144 | brier: 0.02455 | acc: 0.98438:   5%|████▊                                                                                          | 5/100 [00:37<12:00,  7.59s/it]\n",
      "Epoch: 5 | loss: 0.01383 | brier: 0.02619 | acc: 0.98438:   4%|███▊                                                                                          | 4/100 [00:31<12:30,  7.82s/it]\n",
      "Epoch: 5 | loss: 0.00779 | brier: 0.01917 | acc: 0.99219:   4%|███▊                                                                                          | 4/100 [00:31<12:36,  7.88s/it]\n",
      "Epoch: 6 | loss: 0.02396 | brier: 0.02504 | acc: 0.98438:   5%|████▋                                                                                         | 5/100 [00:38<12:03,  7.61s/it]\n",
      "Epoch: 5 | loss: 0.00114 | brier: 0.00727 | acc: 1.0:   4%|███▉                                                                                              | 4/100 [00:30<12:19,  7.70s/it]\n",
      "Epoch: 10 | loss: 0.02296 | brier: 0.02832 | acc: 0.98438:   9%|████████▎                                                                                    | 9/100 [01:01<10:21,  6.83s/it]\n",
      "Epoch: 8 | loss: 0.00985 | brier: 0.01882 | acc: 0.98438:   7%|██████▌                                                                                       | 7/100 [00:47<10:30,  6.78s/it]\n",
      "Epoch: 5 | loss: 0.00194 | brier: 0.00824 | acc: 1.0:   4%|███▉                                                                                              | 4/100 [00:30<12:23,  7.75s/it]\n",
      "Epoch: 6 | loss: 0.00855 | brier: 0.02012 | acc: 0.99219:   5%|████▋                                                                                         | 5/100 [00:36<11:39,  7.37s/it]\n",
      "Epoch: 7 | loss: 0.02463 | brier: 0.03412 | acc: 0.97656:   6%|█████▋                                                                                        | 6/100 [00:42<10:58,  7.00s/it]\n",
      "Epoch: 6 | loss: 0.04482 | brier: 0.04599 | acc: 0.96875:   5%|████▋                                                                                         | 5/100 [00:36<11:25,  7.21s/it]\n",
      "Epoch: 5 | loss: 0.01154 | brier: 0.01647 | acc: 0.98438:   4%|███▊                                                                                          | 4/100 [00:30<12:17,  7.68s/it]\n",
      "Epoch: 5 | loss: 0.01967 | brier: 0.03331 | acc: 0.97656:   4%|███▊                                                                                          | 4/100 [00:30<12:11,  7.62s/it]\n",
      "Epoch: 5 | loss: 0.01487 | brier: 0.03235 | acc: 0.97656:   4%|███▊                                                                                          | 4/100 [00:28<11:27,  7.16s/it]\n",
      "Epoch: 4 | loss: 0.01934 | brier: 0.03416 | acc: 0.97656:   3%|██▊                                                                                           | 3/100 [00:22<12:21,  7.64s/it]\n",
      "Epoch: 7 | loss: 0.00208 | brier: 0.0092 | acc: 1.0:   6%|█████▉                                                                                             | 6/100 [00:40<10:28,  6.69s/it]\n",
      "Epoch: 6 | loss: 0.0014 | brier: 0.00682 | acc: 1.0:   5%|████▉                                                                                              | 5/100 [00:35<11:05,  7.01s/it]\n",
      "Epoch: 7 | loss: 0.00245 | brier: 0.0102 | acc: 1.0:   6%|█████▉                                                                                             | 6/100 [00:40<10:28,  6.68s/it]\n",
      "Epoch: 4 | loss: 0.01907 | brier: 0.03932 | acc: 0.96875:   3%|██▊                                                                                           | 3/100 [00:22<12:21,  7.65s/it]\n",
      "Epoch: 6 | loss: 0.00952 | brier: 0.01856 | acc: 0.99219:   5%|████▋                                                                                         | 5/100 [00:34<10:50,  6.85s/it]\n",
      "Epoch: 4 | loss: 0.00794 | brier: 0.02039 | acc: 0.99219:   3%|██▊                                                                                           | 3/100 [00:23<12:25,  7.69s/it]\n",
      "Epoch: 5 | loss: 0.02378 | brier: 0.02761 | acc: 0.99219:   4%|███▊                                                                                          | 4/100 [00:29<11:41,  7.31s/it]\n",
      "Epoch: 7 | loss: 0.00513 | brier: 0.01207 | acc: 0.99219:   6%|█████▋                                                                                        | 6/100 [00:40<10:35,  6.76s/it]\n"
     ]
    }
   ],
   "source": [
    "from fortuna.loss.classification import cross_entropy_loss_fn, focal_loss_fn\n",
    "from fortuna.metric.classification import compute_counts_confs_accs\n",
    "from fortuna.calib_model import CalibClassifier, Config, Monitor, Optimizer\n",
    "from fortuna.model import LeNet5\n",
    "from fortuna.metric.classification import brier_score, accuracy\n",
    "\n",
    "def brier(preds, uncertainties, targets): \n",
    "    return brier_score(uncertainties, targets)\n",
    "\n",
    "def acc(preds, uncertainties, targets): \n",
    "    return accuracy(preds, targets)\n",
    "\n",
    "calib_model = CalibClassifier(model=LeNet5(output_dim=10))\n",
    "\n",
    "losses = {\"cross entropy\": cross_entropy_loss_fn, \"focal loss\": focal_loss_fn}\n",
    "\n",
    "test_inputs_loader = test_data_loader.to_inputs_loader()\n",
    "all_confs, all_accs, all_eces, = {}, {}, {}\n",
    "\n",
    "n_iter = 30\n",
    "    \n",
    "for i, (name, loss) in enumerate(losses.items()):    \n",
    "    confs, accs, eces = [], [], []\n",
    "    for j in range(n_iter):\n",
    "        status = calib_model.calibrate(\n",
    "            train_data_loader, \n",
    "            val_data_loader=val_data_loader,\n",
    "            loss_fn=loss,\n",
    "            config=Config(\n",
    "                monitor=Monitor(early_stopping_patience=2, metrics=(brier, acc))\n",
    "            )\n",
    "        )\n",
    "\n",
    "        means = calib_model.predictive.mean(test_inputs_loader)\n",
    "        modes = calib_model.predictive.mode(test_inputs_loader)\n",
    "        eces.append(expected_calibration_error(modes, means, targets))\n",
    "        counts, _confs, _accs = compute_counts_confs_accs(modes, means, targets)\n",
    "        confs.append(_confs), accs.append(_accs)\n",
    "    all_confs[name], all_accs[name], all_eces[name] = confs, accs, eces"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f793485",
   "metadata": {},
   "source": [
    "## Expected calibration error and reliability plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779a81f9",
   "metadata": {},
   "source": [
    "We print average and standard deviation of the expected calibration error (ECE) for each loss. Finally, we plot a reliability diagram for each training run and each loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "75acc2e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒═══════════════╤════════════╤════════════╕\n",
      "│ loss / ECE    │       mean │        std │\n",
      "╞═══════════════╪════════════╪════════════╡\n",
      "│ cross entropy │ 0.00912544 │ 0.00212543 │\n",
      "├───────────────┼────────────┼────────────┤\n",
      "│ focal loss    │ 0.0149724  │ 0.00774826 │\n",
      "╘═══════════════╧════════════╧════════════╛\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tabulate import tabulate\n",
    "headers = [\"loss / ECE\", \"mean\", \"std\"]\n",
    "table = [*[[name, np.mean(all_eces[name]), np.std(all_eces[name])] for name in losses.keys()]]\n",
    "print(tabulate(table, headers, tablefmt=\"fancy_grid\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "b27b467f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(6, 3))\n",
    "for i, name in enumerate(losses.keys()):\n",
    "    plt.plot([0, 1], [0, 0], linestyle=\"--\", color=\"gray\", alpha=0.3, label='_nolegend_')\n",
    "    for j, (confs, accs) in enumerate(zip(np.array(all_confs[name]), np.array(all_accs[name]))):\n",
    "        idx = np.where(confs)[0]\n",
    "        plt.plot(confs[idx], confs[idx] - accs[idx], c=f\"C{i}\", alpha=0.5, label='_nolegend_' if j > 0 else name)\n",
    "plt.grid()\n",
    "plt.xlabel(\"confidence\")\n",
    "plt.ylabel(\"confidence - accuracy\")\n",
    "plt.legend(loc=\"lower right\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a86e21",
   "metadata": {},
   "source": [
    "For this simple experiment, the two losses perform similarly well, as the respective mean ECEs are not significantly different. We can notice though that the cross-entropy loss appears slightly overconfident, while the focal loss is slight underconfidence. This aligns with the derivation in [Mukhoti et al., 2020](https://proceedings.neurips.cc/paper/2020/file/aeb7b30ef1d024a76f21a1d40e30c302-Paper.pdf), showing that, compared to the cross-entropy loss, the focal loss avoids overconfidence as it intrinsically maximizes an entropy term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57e16b9e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fortuna",
   "language": "python",
   "name": "fortuna"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
